The optimization of gear shifting is a critical process in heavy-duty trucks for
adjusting engine operating points, enabling a multi-objective balance between
power, fuel efficiency, and comfort. However, this process is challenged by the
nonlinear characteristics of engine fuel consumption, power interruptions during
AMT (Automated Manual Transmission) shifts, and uncertainties in driving
conditions. This study proposes a rolling optimization shift strategy for heavy
trucks equipped with AMT, based on a multi-scale prediction of internal
combustion engine fuel consumption on the road.
Firstly, a predictive model for the energy efficiency and dynamics of heavy-duty
trucks with AMT was developed, accounting for the engine’s engine’s operating
condition points and power interruptions during shifting. Secondly, a future
power demand, vehicle speed, and fuel consumption prediction algorithm was
designed, iterating based on accelerator pedal position forecasts and dynamic
modeling. Finally, integrating the predicted future conditions, fuel
consumption, and road characteristics, a cloud-assisted optimal gear rolling
optimization algorithm was established, achieving a compromise between fuel
consumption, shift frequency, and vehicle performance.
Simulation results on the GT-SUITE platform indicated that, compared with the
rule-based shifting strategy in the ECU (Electronic Control Unit), the proposed
method reduces fuel consumption by 2.1 % and shift frequency by 15.9 % under the
C-WTVC. (C-WTVC refers to a driving cycle based on the World Transient Vehicle
Cycle (WTVC) for heavy-duty commercial vehicles. It is modified by adjusting
acceleration and deceleration to create a driving profile.) Road tests on
heavy-duty trucks demonstrated fuel savings of 10.52 % in a 2-kilometer full
acceleration scenario with 20%pedal, 7.26%savings with 90%pedal, and 4.32 %
savings under free driving conditions. These results confirmed the effectiveness
of the proposed method.